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Glaucoma: Overview

Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...

Angle Closure Glaucoma: Treatment

Angle Closure Glaucoma: Treatment

Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...

Open Angle Glaucoma: Treatment

Open Angle Glaucoma: Treatment

In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...

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Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural

Hyeonsung Cho¹, Young Hoon Hwang², Jae Keun Chung²

¹Intelligence and Robot System Research Group, Electronics & Telecommunication Research Institute, Daejeon, Republic of Korea.

Current Eye Research

|April 6, 2021

Summary

A novel deep learning ensemble method accurately grades glaucoma severity from fundus photographs. This approach outperforms single models, offering a stable clinical decision support tool for early glaucoma detection.

Keywords:

Artificial intelligence deep learning diagnostic imaging glaucoma neural networks models

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Glaucoma is a leading cause of irreversible blindness worldwide.
Accurate staging of glaucoma is crucial for timely intervention and management.
Automated grading systems can aid in clinical decision-making and screening.

Purpose of the Study:

To develop and evaluate a deep learning ensemble method for automated glaucoma staging.
To assess the performance of the ensemble model compared to single convolutional neural network (CNN) models.
To determine the utility of the method as a clinical decision support system.

Main Methods:

A dataset of 3,460 fundus photographs from 2,204 patients was utilized.
Fundus images were classified into unaffected, early-stage, and late-stage glaucoma.
An ensemble system combined 56 different CNN models for enhanced performance.

Main Results:

The ensemble method achieved 88.1% accuracy and an average area under the receiver operating characteristic (AUROC) of 0.975.
This significantly outperformed the best single CNN model (85.2% accuracy, 0.950 AUROC).
The ensemble method demonstrated fewer false negative mispredictions.

Conclusions:

Averaging multiple CNN models improves glaucoma stage classification from fundus photographs.
The ensemble method shows potential as a clinical decision support system for glaucoma screening.
The approach provides high, stable performance even with limited data.